Overview

Dataset statistics

Number of variables19
Number of observations529
Missing cells1066
Missing cells (%)10.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.7 KiB
Average record size in memory152.2 B

Variable types

Numeric9
Categorical10

Alerts

nbinsta is highly overall correlated with nbtik and 1 other fieldsHigh correlation
nbtwit is highly overall correlated with twitterHigh correlation
nbsnap is highly overall correlated with snapchatHigh correlation
nbtik is highly overall correlated with nbinsta and 1 other fieldsHigh correlation
googp is highly overall correlated with googmpHigh correlation
googmp is highly overall correlated with googpHigh correlation
instagra is highly overall correlated with nbinstaHigh correlation
twitter is highly overall correlated with nbtwitHigh correlation
snapchat is highly overall correlated with nbsnapHigh correlation
tiktok is highly overall correlated with nbtikHigh correlation
instagra is highly imbalanced (79.4%)Imbalance
nbinsta has 21 (4.0%) missing valuesMissing
nbtwit has 372 (70.3%) missing valuesMissing
snapchat has 7 (1.3%) missing valuesMissing
nbsnap has 259 (49.0%) missing valuesMissing
nbtik has 332 (62.8%) missing valuesMissing
instap has 8 (1.5%) missing valuesMissing
snapp has 13 (2.5%) missing valuesMissing
googp has 10 (1.9%) missing valuesMissing
googmp has 10 (1.9%) missing valuesMissing
random_id4 has unique valuesUnique
nbtwit has 19 (3.6%) zerosZeros
instap has 222 (42.0%) zerosZeros
snapp has 421 (79.6%) zerosZeros
googp has 143 (27.0%) zerosZeros
googmp has 190 (35.9%) zerosZeros

Reproduction

Analysis started2024-02-06 10:12:42.794866
Analysis finished2024-02-06 10:12:47.146318
Duration4.35 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

random_id4
Real number (ℝ)

UNIQUE 

Distinct529
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5116062.9
Minimum37320
Maximum9973576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:47.195291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37320
5-th percentile647120
Q12925179
median5121654
Q37503167
95-th percentile9217394.2
Maximum9973576
Range9936256
Interquartile range (IQR)4577988

Descriptive statistics

Standard deviation2788763.2
Coefficient of variation (CV)0.54509948
Kurtosis-1.1307902
Mean5116062.9
Median Absolute Deviation (MAD)2330145
Skewness-0.073446117
Sum2.7063972 × 109
Variance7.7772001 × 1012
MonotonicityStrictly increasing
2024-02-06T11:12:47.258153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37320 1
 
0.2%
6583444 1
 
0.2%
6967343 1
 
0.2%
6953254 1
 
0.2%
6865922 1
 
0.2%
6837858 1
 
0.2%
6820649 1
 
0.2%
6818054 1
 
0.2%
6757890 1
 
0.2%
6752959 1
 
0.2%
Other values (519) 519
98.1%
ValueCountFrequency (%)
37320 1
0.2%
75227 1
0.2%
78695 1
0.2%
80831 1
0.2%
140145 1
0.2%
142591 1
0.2%
154015 1
0.2%
157492 1
0.2%
180228 1
0.2%
215268 1
0.2%
ValueCountFrequency (%)
9973576 1
0.2%
9956964 1
0.2%
9947814 1
0.2%
9940978 1
0.2%
9928746 1
0.2%
9897608 1
0.2%
9816676 1
0.2%
9801565 1
0.2%
9745640 1
0.2%
9741187 1
0.2%

survey
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
ScPoBx_1A
299 
ScPoBx_3A
230 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4761
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScPoBx_3A
2nd rowScPoBx_3A
3rd rowScPoBx_3A
4th rowScPoBx_1A
5th rowScPoBx_1A

Common Values

ValueCountFrequency (%)
ScPoBx_1A 299
56.5%
ScPoBx_3A 230
43.5%

Length

2024-02-06T11:12:47.308401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:47.345454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
scpobx_1a 299
56.5%
scpobx_3a 230
43.5%

Most occurring characters

ValueCountFrequency (%)
S 529
11.1%
c 529
11.1%
P 529
11.1%
o 529
11.1%
B 529
11.1%
x 529
11.1%
_ 529
11.1%
A 529
11.1%
1 299
6.3%
3 230
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2116
44.4%
Lowercase Letter 1587
33.3%
Connector Punctuation 529
 
11.1%
Decimal Number 529
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 529
25.0%
P 529
25.0%
B 529
25.0%
A 529
25.0%
Lowercase Letter
ValueCountFrequency (%)
c 529
33.3%
o 529
33.3%
x 529
33.3%
Decimal Number
ValueCountFrequency (%)
1 299
56.5%
3 230
43.5%
Connector Punctuation
ValueCountFrequency (%)
_ 529
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3703
77.8%
Common 1058
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 529
14.3%
c 529
14.3%
P 529
14.3%
o 529
14.3%
B 529
14.3%
x 529
14.3%
A 529
14.3%
Common
ValueCountFrequency (%)
_ 529
50.0%
1 299
28.3%
3 230
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 529
11.1%
c 529
11.1%
P 529
11.1%
o 529
11.1%
B 529
11.1%
x 529
11.1%
_ 529
11.1%
A 529
11.1%
1 299
6.3%
3 230
4.8%

tps_rs
Categorical

Distinct5
Distinct (%)1.0%
Missing4
Missing (%)0.8%
Memory size4.3 KiB
3.0
206 
2.0
172 
1.0
76 
4.0
59 
5.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1575
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 206
38.9%
2.0 172
32.5%
1.0 76
 
14.4%
4.0 59
 
11.2%
5.0 12
 
2.3%
(Missing) 4
 
0.8%

Length

2024-02-06T11:12:47.386157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:47.426243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 206
39.2%
2.0 172
32.8%
1.0 76
 
14.5%
4.0 59
 
11.2%
5.0 12
 
2.3%

Most occurring characters

ValueCountFrequency (%)
. 525
33.3%
0 525
33.3%
3 206
 
13.1%
2 172
 
10.9%
1 76
 
4.8%
4 59
 
3.7%
5 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1050
66.7%
Other Punctuation 525
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 525
50.0%
3 206
 
19.6%
2 172
 
16.4%
1 76
 
7.2%
4 59
 
5.6%
5 12
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 525
33.3%
0 525
33.3%
3 206
 
13.1%
2 172
 
10.9%
1 76
 
4.8%
4 59
 
3.7%
5 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 525
33.3%
0 525
33.3%
3 206
 
13.1%
2 172
 
10.9%
1 76
 
4.8%
4 59
 
3.7%
5 12
 
0.8%

instagra
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing3
Missing (%)0.6%
Memory size4.3 KiB
J'ai
509 
Je n'ai pas
 
17

Length

Max length11
Median length4
Mean length4.2262357
Min length4

Characters and Unicode

Total characters2223
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ'ai
2nd rowJ'ai
3rd rowJ'ai
4th rowJ'ai
5th rowJ'ai

Common Values

ValueCountFrequency (%)
J'ai 509
96.2%
Je n'ai pas 17
 
3.2%
(Missing) 3
 
0.6%

Length

2024-02-06T11:12:47.475963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:47.515193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
j'ai 509
90.9%
je 17
 
3.0%
n'ai 17
 
3.0%
pas 17
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a 543
24.4%
J 526
23.7%
' 526
23.7%
i 526
23.7%
34
 
1.5%
e 17
 
0.8%
n 17
 
0.8%
p 17
 
0.8%
s 17
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1137
51.1%
Uppercase Letter 526
23.7%
Other Punctuation 526
23.7%
Space Separator 34
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 543
47.8%
i 526
46.3%
e 17
 
1.5%
n 17
 
1.5%
p 17
 
1.5%
s 17
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
J 526
100.0%
Other Punctuation
ValueCountFrequency (%)
' 526
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1663
74.8%
Common 560
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 543
32.7%
J 526
31.6%
i 526
31.6%
e 17
 
1.0%
n 17
 
1.0%
p 17
 
1.0%
s 17
 
1.0%
Common
ValueCountFrequency (%)
' 526
93.9%
34
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 543
24.4%
J 526
23.7%
' 526
23.7%
i 526
23.7%
34
 
1.5%
e 17
 
0.8%
n 17
 
0.8%
p 17
 
0.8%
s 17
 
0.8%

nbinsta
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct280
Distinct (%)55.1%
Missing21
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean438.31299
Minimum18
Maximum3758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:47.558642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile91.35
Q1200
median354.5
Q3580
95-th percentile1034.75
Maximum3758
Range3740
Interquartile range (IQR)380

Descriptive statistics

Standard deviation352.96269
Coefficient of variation (CV)0.80527543
Kurtosis20.310765
Mean438.31299
Median Absolute Deviation (MAD)161
Skewness3.165841
Sum222663
Variance124582.66
MonotonicityNot monotonic
2024-02-06T11:12:47.617712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 19
 
3.6%
500 18
 
3.4%
300 16
 
3.0%
200 16
 
3.0%
150 12
 
2.3%
600 11
 
2.1%
450 10
 
1.9%
250 10
 
1.9%
350 8
 
1.5%
900 7
 
1.3%
Other values (270) 381
72.0%
(Missing) 21
 
4.0%
ValueCountFrequency (%)
18 1
 
0.2%
30 1
 
0.2%
37 1
 
0.2%
38 1
 
0.2%
39 1
 
0.2%
50 1
 
0.2%
53 1
 
0.2%
54 1
 
0.2%
60 4
0.8%
70 2
0.4%
ValueCountFrequency (%)
3758 1
 
0.2%
2888 1
 
0.2%
2000 1
 
0.2%
1750 1
 
0.2%
1600 2
0.4%
1524 1
 
0.2%
1400 2
0.4%
1300 3
0.6%
1235 1
 
0.2%
1200 3
0.6%

twitter
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing5
Missing (%)0.9%
Memory size4.3 KiB
Je n'ai pas
365 
J'ai
159 

Length

Max length11
Median length11
Mean length8.8759542
Min length4

Characters and Unicode

Total characters4651
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJe n'ai pas
2nd rowJe n'ai pas
3rd rowJ'ai
4th rowJe n'ai pas
5th rowJe n'ai pas

Common Values

ValueCountFrequency (%)
Je n'ai pas 365
69.0%
J'ai 159
30.1%
(Missing) 5
 
0.9%

Length

2024-02-06T11:12:47.672286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:47.713760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
je 365
29.1%
n'ai 365
29.1%
pas 365
29.1%
j'ai 159
12.7%

Most occurring characters

ValueCountFrequency (%)
a 889
19.1%
730
15.7%
J 524
11.3%
' 524
11.3%
i 524
11.3%
e 365
7.8%
n 365
7.8%
p 365
7.8%
s 365
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2873
61.8%
Space Separator 730
 
15.7%
Uppercase Letter 524
 
11.3%
Other Punctuation 524
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 889
30.9%
i 524
18.2%
e 365
12.7%
n 365
12.7%
p 365
12.7%
s 365
12.7%
Space Separator
ValueCountFrequency (%)
730
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 524
100.0%
Other Punctuation
ValueCountFrequency (%)
' 524
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3397
73.0%
Common 1254
 
27.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 889
26.2%
J 524
15.4%
i 524
15.4%
e 365
10.7%
n 365
10.7%
p 365
10.7%
s 365
10.7%
Common
ValueCountFrequency (%)
730
58.2%
' 524
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 889
19.1%
730
15.7%
J 524
11.3%
' 524
11.3%
i 524
11.3%
e 365
7.8%
n 365
7.8%
p 365
7.8%
s 365
7.8%

nbtwit
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)40.1%
Missing372
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean66.745223
Minimum0
Maximum1907
Zeros19
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:47.758655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q350
95-th percentile214.2
Maximum1907
Range1907
Interquartile range (IQR)47

Descriptive statistics

Standard deviation191.64858
Coefficient of variation (CV)2.8713453
Kurtosis59.520178
Mean66.745223
Median Absolute Deviation (MAD)14
Skewness7.0304081
Sum10479
Variance36729.178
MonotonicityNot monotonic
2024-02-06T11:12:47.818851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
3.6%
3 10
 
1.9%
2 10
 
1.9%
10 10
 
1.9%
50 7
 
1.3%
20 7
 
1.3%
30 6
 
1.1%
150 5
 
0.9%
1 4
 
0.8%
15 4
 
0.8%
Other values (53) 75
 
14.2%
(Missing) 372
70.3%
ValueCountFrequency (%)
0 19
3.6%
1 4
 
0.8%
2 10
1.9%
3 10
1.9%
4 3
 
0.6%
5 4
 
0.8%
6 3
 
0.6%
7 2
 
0.4%
8 1
 
0.2%
9 2
 
0.4%
ValueCountFrequency (%)
1907 1
0.2%
1050 1
0.2%
763 1
0.2%
400 1
0.2%
364 1
0.2%
351 1
0.2%
300 1
0.2%
271 1
0.2%
200 2
0.4%
193 1
0.2%

snapchat
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing7
Missing (%)1.3%
Memory size4.3 KiB
J'ai
277 
Je n'ai pas
245 

Length

Max length11
Median length4
Mean length7.2854406
Min length4

Characters and Unicode

Total characters3803
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJe n'ai pas
2nd rowJ'ai
3rd rowJe n'ai pas
4th rowJ'ai
5th rowJ'ai

Common Values

ValueCountFrequency (%)
J'ai 277
52.4%
Je n'ai pas 245
46.3%
(Missing) 7
 
1.3%

Length

2024-02-06T11:12:47.875753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:47.917467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
j'ai 277
27.4%
je 245
24.2%
n'ai 245
24.2%
pas 245
24.2%

Most occurring characters

ValueCountFrequency (%)
a 767
20.2%
J 522
13.7%
' 522
13.7%
i 522
13.7%
490
12.9%
e 245
 
6.4%
n 245
 
6.4%
p 245
 
6.4%
s 245
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2269
59.7%
Uppercase Letter 522
 
13.7%
Other Punctuation 522
 
13.7%
Space Separator 490
 
12.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 767
33.8%
i 522
23.0%
e 245
 
10.8%
n 245
 
10.8%
p 245
 
10.8%
s 245
 
10.8%
Uppercase Letter
ValueCountFrequency (%)
J 522
100.0%
Other Punctuation
ValueCountFrequency (%)
' 522
100.0%
Space Separator
ValueCountFrequency (%)
490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2791
73.4%
Common 1012
 
26.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 767
27.5%
J 522
18.7%
i 522
18.7%
e 245
 
8.8%
n 245
 
8.8%
p 245
 
8.8%
s 245
 
8.8%
Common
ValueCountFrequency (%)
' 522
51.6%
490
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 767
20.2%
J 522
13.7%
' 522
13.7%
i 522
13.7%
490
12.9%
e 245
 
6.4%
n 245
 
6.4%
p 245
 
6.4%
s 245
 
6.4%

nbsnap
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)23.0%
Missing259
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean126.52963
Minimum0
Maximum2000
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:47.965993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q140
median100
Q3150
95-th percentile327.5
Maximum2000
Range2000
Interquartile range (IQR)110

Descriptive statistics

Standard deviation171.41245
Coefficient of variation (CV)1.3547218
Kurtosis55.922819
Mean126.52963
Median Absolute Deviation (MAD)50
Skewness6.0736892
Sum34163
Variance29382.228
MonotonicityNot monotonic
2024-02-06T11:12:48.027522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 38
 
7.2%
50 30
 
5.7%
150 27
 
5.1%
200 23
 
4.3%
30 19
 
3.6%
40 15
 
2.8%
20 13
 
2.5%
60 11
 
2.1%
10 9
 
1.7%
300 9
 
1.7%
Other values (52) 76
 
14.4%
(Missing) 259
49.0%
ValueCountFrequency (%)
0 1
 
0.2%
3 2
 
0.4%
4 1
 
0.2%
5 1
 
0.2%
8 1
 
0.2%
10 9
1.7%
12 1
 
0.2%
15 3
 
0.6%
20 13
2.5%
28 1
 
0.2%
ValueCountFrequency (%)
2000 1
 
0.2%
1000 1
 
0.2%
700 2
0.4%
615 1
 
0.2%
600 3
0.6%
500 1
 
0.2%
450 1
 
0.2%
400 1
 
0.2%
354 1
 
0.2%
350 2
0.4%

tiktok
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing4
Missing (%)0.8%
Memory size4.3 KiB
Je n'ai pas
326 
J'ai
199 

Length

Max length11
Median length11
Mean length8.3466667
Min length4

Characters and Unicode

Total characters4382
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJe n'ai pas
2nd rowJ'ai
3rd rowJ'ai
4th rowJ'ai
5th rowJ'ai

Common Values

ValueCountFrequency (%)
Je n'ai pas 326
61.6%
J'ai 199
37.6%
(Missing) 4
 
0.8%

Length

2024-02-06T11:12:48.084867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:48.127359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
je 326
27.7%
n'ai 326
27.7%
pas 326
27.7%
j'ai 199
16.9%

Most occurring characters

ValueCountFrequency (%)
a 851
19.4%
652
14.9%
J 525
12.0%
' 525
12.0%
i 525
12.0%
e 326
 
7.4%
n 326
 
7.4%
p 326
 
7.4%
s 326
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2680
61.2%
Space Separator 652
 
14.9%
Uppercase Letter 525
 
12.0%
Other Punctuation 525
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 851
31.8%
i 525
19.6%
e 326
 
12.2%
n 326
 
12.2%
p 326
 
12.2%
s 326
 
12.2%
Space Separator
ValueCountFrequency (%)
652
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 525
100.0%
Other Punctuation
ValueCountFrequency (%)
' 525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3205
73.1%
Common 1177
 
26.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 851
26.6%
J 525
16.4%
i 525
16.4%
e 326
 
10.2%
n 326
 
10.2%
p 326
 
10.2%
s 326
 
10.2%
Common
ValueCountFrequency (%)
652
55.4%
' 525
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 851
19.4%
652
14.9%
J 525
12.0%
' 525
12.0%
i 525
12.0%
e 326
 
7.4%
n 326
 
7.4%
p 326
 
7.4%
s 326
 
7.4%

nbtik
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)45.2%
Missing332
Missing (%)62.8%
Infinite0
Infinite (%)0.0%
Mean866.94924
Minimum0
Maximum64400
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:48.171796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q115
median35
Q3150
95-th percentile1260
Maximum64400
Range64400
Interquartile range (IQR)135

Descriptive statistics

Standard deviation5830.7733
Coefficient of variation (CV)6.7256225
Kurtosis85.585495
Mean866.94924
Median Absolute Deviation (MAD)30
Skewness8.9725812
Sum170789
Variance33997917
MonotonicityNot monotonic
2024-02-06T11:12:48.227614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 13
 
2.5%
30 12
 
2.3%
10 11
 
2.1%
15 10
 
1.9%
5 9
 
1.7%
200 8
 
1.5%
50 8
 
1.5%
100 7
 
1.3%
40 7
 
1.3%
0 5
 
0.9%
Other values (79) 107
 
20.2%
(Missing) 332
62.8%
ValueCountFrequency (%)
0 5
0.9%
1 1
 
0.2%
2 4
 
0.8%
3 4
 
0.8%
4 4
 
0.8%
5 9
1.7%
6 2
 
0.4%
7 1
 
0.2%
8 1
 
0.2%
10 11
2.1%
ValueCountFrequency (%)
64400 1
0.2%
39000 1
0.2%
33000 1
0.2%
5400 1
0.2%
2100 1
0.2%
2000 1
0.2%
1600 1
0.2%
1500 1
0.2%
1400 1
0.2%
1300 1
0.2%

instap
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)3.1%
Missing8
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.3071017
Minimum0
Maximum50
Zeros222
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:48.274961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile10
Maximum50
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8136153
Coefficient of variation (CV)1.4555389
Kurtosis22.238753
Mean3.3071017
Median Absolute Deviation (MAD)2
Skewness3.4944337
Sum1723
Variance23.170892
MonotonicityNot monotonic
2024-02-06T11:12:48.318715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 222
42.0%
5 108
20.4%
10 49
 
9.3%
2 41
 
7.8%
3 36
 
6.8%
1 27
 
5.1%
15 10
 
1.9%
4 9
 
1.7%
7 4
 
0.8%
6 4
 
0.8%
Other values (6) 11
 
2.1%
(Missing) 8
 
1.5%
ValueCountFrequency (%)
0 222
42.0%
1 27
 
5.1%
2 41
 
7.8%
3 36
 
6.8%
4 9
 
1.7%
5 108
20.4%
6 4
 
0.8%
7 4
 
0.8%
8 3
 
0.6%
9 1
 
0.2%
ValueCountFrequency (%)
50 1
 
0.2%
30 3
 
0.6%
25 1
 
0.2%
20 2
 
0.4%
15 10
 
1.9%
10 49
9.3%
9 1
 
0.2%
8 3
 
0.6%
7 4
 
0.8%
6 4
 
0.8%

snapp
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)2.1%
Missing13
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean0.72286822
Minimum0
Maximum20
Zeros421
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:48.361305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2021041
Coefficient of variation (CV)3.0463424
Kurtosis29.717096
Mean0.72286822
Median Absolute Deviation (MAD)0
Skewness4.8115274
Sum373
Variance4.8492624
MonotonicityNot monotonic
2024-02-06T11:12:48.404892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 421
79.6%
2 26
 
4.9%
1 23
 
4.3%
5 21
 
4.0%
3 9
 
1.7%
10 8
 
1.5%
8 2
 
0.4%
20 2
 
0.4%
4 2
 
0.4%
15 1
 
0.2%
(Missing) 13
 
2.5%
ValueCountFrequency (%)
0 421
79.6%
1 23
 
4.3%
2 26
 
4.9%
3 9
 
1.7%
4 2
 
0.4%
5 21
 
4.0%
7 1
 
0.2%
8 2
 
0.4%
10 8
 
1.5%
15 1
 
0.2%
ValueCountFrequency (%)
20 2
 
0.4%
15 1
 
0.2%
10 8
 
1.5%
8 2
 
0.4%
7 1
 
0.2%
5 21
4.0%
4 2
 
0.4%
3 9
 
1.7%
2 26
4.9%
1 23
4.3%

googp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)4.6%
Missing10
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean7.8073218
Minimum0
Maximum150
Zeros143
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:48.451011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile25
Maximum150
Range150
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.69563
Coefficient of variation (CV)1.6261184
Kurtosis43.218799
Mean7.8073218
Median Absolute Deviation (MAD)5
Skewness5.351079
Sum4052
Variance161.17902
MonotonicityNot monotonic
2024-02-06T11:12:48.496978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 143
27.0%
5 106
20.0%
10 96
18.1%
15 36
 
6.8%
2 34
 
6.4%
20 20
 
3.8%
1 19
 
3.6%
3 16
 
3.0%
50 9
 
1.7%
30 8
 
1.5%
Other values (14) 32
 
6.0%
(Missing) 10
 
1.9%
ValueCountFrequency (%)
0 143
27.0%
1 19
 
3.6%
2 34
 
6.4%
3 16
 
3.0%
4 3
 
0.6%
5 106
20.0%
6 1
 
0.2%
7 6
 
1.1%
8 6
 
1.1%
9 2
 
0.4%
ValueCountFrequency (%)
150 1
 
0.2%
100 2
 
0.4%
80 1
 
0.2%
50 9
 
1.7%
40 2
 
0.4%
35 1
 
0.2%
30 8
 
1.5%
25 3
 
0.6%
20 20
3.8%
15 36
6.8%

googmp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)3.9%
Missing10
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean4.955684
Minimum0
Maximum100
Zeros190
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-02-06T11:12:48.671398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile15
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.5254537
Coefficient of variation (CV)1.7203384
Kurtosis46.365913
Mean4.955684
Median Absolute Deviation (MAD)2
Skewness5.4879474
Sum2572
Variance72.68336
MonotonicityNot monotonic
2024-02-06T11:12:48.718097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 190
35.9%
5 77
14.6%
10 67
 
12.7%
2 57
 
10.8%
3 27
 
5.1%
1 26
 
4.9%
15 22
 
4.2%
20 14
 
2.6%
8 10
 
1.9%
7 9
 
1.7%
Other values (10) 20
 
3.8%
(Missing) 10
 
1.9%
ValueCountFrequency (%)
0 190
35.9%
1 26
 
4.9%
2 57
 
10.8%
3 27
 
5.1%
4 6
 
1.1%
5 77
14.6%
6 2
 
0.4%
7 9
 
1.7%
8 10
 
1.9%
9 1
 
0.2%
ValueCountFrequency (%)
100 1
 
0.2%
80 1
 
0.2%
60 1
 
0.2%
50 2
 
0.4%
30 4
 
0.8%
25 1
 
0.2%
20 14
 
2.6%
15 22
 
4.2%
13 1
 
0.2%
10 67
12.7%

CS05
Categorical

Distinct4
Distinct (%)0.8%
Missing5
Missing (%)0.9%
Memory size4.3 KiB
Assez bien
291 
Très bien
202 
Pas bien
 
27
Pas intégré.e du tout
 
4

Length

Max length21
Median length10
Mean length9.5954198
Min length8

Characters and Unicode

Total characters5028
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAssez bien
2nd rowAssez bien
3rd rowTrès bien
4th rowAssez bien
5th rowAssez bien

Common Values

ValueCountFrequency (%)
Assez bien 291
55.0%
Très bien 202
38.2%
Pas bien 27
 
5.1%
Pas intégré.e du tout 4
 
0.8%
(Missing) 5
 
0.9%

Length

2024-02-06T11:12:48.768964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:48.809444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bien 520
49.2%
assez 291
27.6%
très 202
 
19.1%
pas 31
 
2.9%
intégré.e 4
 
0.4%
du 4
 
0.4%
tout 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 815
16.2%
s 815
16.2%
532
10.6%
i 524
10.4%
n 524
10.4%
b 520
10.3%
A 291
 
5.8%
z 291
 
5.8%
r 206
 
4.1%
è 202
 
4.0%
Other values (10) 308
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3968
78.9%
Space Separator 532
 
10.6%
Uppercase Letter 524
 
10.4%
Other Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 815
20.5%
s 815
20.5%
i 524
13.2%
n 524
13.2%
b 520
13.1%
z 291
 
7.3%
r 206
 
5.2%
è 202
 
5.1%
a 31
 
0.8%
t 12
 
0.3%
Other values (5) 28
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
A 291
55.5%
T 202
38.5%
P 31
 
5.9%
Space Separator
ValueCountFrequency (%)
532
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4492
89.3%
Common 536
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 815
18.1%
s 815
18.1%
i 524
11.7%
n 524
11.7%
b 520
11.6%
A 291
 
6.5%
z 291
 
6.5%
r 206
 
4.6%
è 202
 
4.5%
T 202
 
4.5%
Other values (8) 102
 
2.3%
Common
ValueCountFrequency (%)
532
99.3%
. 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4818
95.8%
None 210
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 815
16.9%
s 815
16.9%
532
11.0%
i 524
10.9%
n 524
10.9%
b 520
10.8%
A 291
 
6.0%
z 291
 
6.0%
r 206
 
4.3%
T 202
 
4.2%
Other values (8) 98
 
2.0%
None
ValueCountFrequency (%)
è 202
96.2%
é 8
 
3.8%

CS12
Categorical

Distinct3
Distinct (%)0.6%
Missing5
Missing (%)0.9%
Memory size4.3 KiB
Parfois
301 
Souvent
171 
Jamais
52 

Length

Max length7
Median length7
Mean length6.9007634
Min length6

Characters and Unicode

Total characters3616
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouvent
2nd rowSouvent
3rd rowParfois
4th rowSouvent
5th rowParfois

Common Values

ValueCountFrequency (%)
Parfois 301
56.9%
Souvent 171
32.3%
Jamais 52
 
9.8%
(Missing) 5
 
0.9%

Length

2024-02-06T11:12:48.855794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:48.896910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
parfois 301
57.4%
souvent 171
32.6%
jamais 52
 
9.9%

Most occurring characters

ValueCountFrequency (%)
o 472
13.1%
a 405
11.2%
i 353
9.8%
s 353
9.8%
P 301
8.3%
r 301
8.3%
f 301
8.3%
S 171
 
4.7%
u 171
 
4.7%
v 171
 
4.7%
Other values (5) 617
17.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3092
85.5%
Uppercase Letter 524
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 472
15.3%
a 405
13.1%
i 353
11.4%
s 353
11.4%
r 301
9.7%
f 301
9.7%
u 171
 
5.5%
v 171
 
5.5%
e 171
 
5.5%
n 171
 
5.5%
Other values (2) 223
7.2%
Uppercase Letter
ValueCountFrequency (%)
P 301
57.4%
S 171
32.6%
J 52
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3616
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 472
13.1%
a 405
11.2%
i 353
9.8%
s 353
9.8%
P 301
8.3%
r 301
8.3%
f 301
8.3%
S 171
 
4.7%
u 171
 
4.7%
v 171
 
4.7%
Other values (5) 617
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 472
13.1%
a 405
11.2%
i 353
9.8%
s 353
9.8%
P 301
8.3%
r 301
8.3%
f 301
8.3%
S 171
 
4.7%
u 171
 
4.7%
v 171
 
4.7%
Other values (5) 617
17.1%

resid2
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size4.3 KiB
Bordeaux
249 
Pessac / Talence / Gradignan
247 
Autre commune
30 

Length

Max length28
Median length13
Mean length17.676806
Min length8

Characters and Unicode

Total characters9298
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBordeaux
2nd rowBordeaux
3rd rowBordeaux
4th rowPessac / Talence / Gradignan
5th rowAutre commune

Common Values

ValueCountFrequency (%)
Bordeaux 249
47.1%
Pessac / Talence / Gradignan 247
46.7%
Autre commune 30
 
5.7%
(Missing) 3
 
0.6%

Length

2024-02-06T11:12:48.958529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:49.009270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
494
32.0%
bordeaux 249
16.1%
pessac 247
16.0%
talence 247
16.0%
gradignan 247
16.0%
autre 30
 
1.9%
commune 30
 
1.9%

Most occurring characters

ValueCountFrequency (%)
a 1237
13.3%
e 1050
11.3%
1018
10.9%
n 771
 
8.3%
r 526
 
5.7%
c 524
 
5.6%
d 496
 
5.3%
/ 494
 
5.3%
s 494
 
5.3%
u 309
 
3.3%
Other values (12) 2379
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6766
72.8%
Uppercase Letter 1020
 
11.0%
Space Separator 1018
 
10.9%
Other Punctuation 494
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1237
18.3%
e 1050
15.5%
n 771
11.4%
r 526
7.8%
c 524
7.7%
d 496
7.3%
s 494
 
7.3%
u 309
 
4.6%
o 279
 
4.1%
x 249
 
3.7%
Other values (5) 831
12.3%
Uppercase Letter
ValueCountFrequency (%)
B 249
24.4%
P 247
24.2%
T 247
24.2%
G 247
24.2%
A 30
 
2.9%
Space Separator
ValueCountFrequency (%)
1018
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7786
83.7%
Common 1512
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1237
15.9%
e 1050
13.5%
n 771
9.9%
r 526
 
6.8%
c 524
 
6.7%
d 496
 
6.4%
s 494
 
6.3%
u 309
 
4.0%
o 279
 
3.6%
B 249
 
3.2%
Other values (10) 1851
23.8%
Common
ValueCountFrequency (%)
1018
67.3%
/ 494
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1237
13.3%
e 1050
11.3%
1018
10.9%
n 771
 
8.3%
r 526
 
5.7%
c 524
 
5.6%
d 496
 
5.3%
/ 494
 
5.3%
s 494
 
5.3%
u 309
 
3.3%
Other values (12) 2379
25.6%

resid6
Categorical

Distinct6
Distinct (%)1.1%
Missing5
Missing (%)0.9%
Memory size4.3 KiB
Commune rurale
177 
- de 20 000 habitants
107 
20 000 à 99 999 habitants
91 
100 000 habitats et plus
73 
Autre pays que la France
43 

Length

Max length25
Median length24
Mean length20.183206
Min length14

Characters and Unicode

Total characters10576
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row- de 20 000 habitants
2nd rowAgglomération parisienne
3rd rowCommune rurale
4th row100 000 habitats et plus
5th row- de 20 000 habitants

Common Values

ValueCountFrequency (%)
Commune rurale 177
33.5%
- de 20 000 habitants 107
20.2%
20 000 à 99 999 habitants 91
17.2%
100 000 habitats et plus 73
13.8%
Autre pays que la France 43
 
8.1%
Agglomération parisienne 33
 
6.2%
(Missing) 5
 
0.9%

Length

2024-02-06T11:12:49.068349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T11:12:49.112882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000 271
13.0%
20 198
 
9.5%
habitants 198
 
9.5%
commune 177
 
8.5%
rurale 177
 
8.5%
107
 
5.1%
de 107
 
5.1%
à 91
 
4.4%
99 91
 
4.4%
999 91
 
4.4%
Other values (11) 573
27.5%

Most occurring characters

ValueCountFrequency (%)
1557
14.7%
0 1157
 
10.9%
a 914
 
8.6%
e 729
 
6.9%
t 691
 
6.5%
n 517
 
4.9%
u 513
 
4.9%
r 506
 
4.8%
9 455
 
4.3%
s 420
 
4.0%
Other values (20) 3117
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6733
63.7%
Decimal Number 1883
 
17.8%
Space Separator 1557
 
14.7%
Uppercase Letter 296
 
2.8%
Dash Punctuation 107
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 914
13.6%
e 729
10.8%
t 691
10.3%
n 517
 
7.7%
u 513
 
7.6%
r 506
 
7.5%
s 420
 
6.2%
m 387
 
5.7%
i 370
 
5.5%
l 326
 
4.8%
Other values (11) 1360
20.2%
Decimal Number
ValueCountFrequency (%)
0 1157
61.4%
9 455
 
24.2%
2 198
 
10.5%
1 73
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
C 177
59.8%
A 76
25.7%
F 43
 
14.5%
Space Separator
ValueCountFrequency (%)
1557
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7029
66.5%
Common 3547
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 914
13.0%
e 729
10.4%
t 691
9.8%
n 517
 
7.4%
u 513
 
7.3%
r 506
 
7.2%
s 420
 
6.0%
m 387
 
5.5%
i 370
 
5.3%
l 326
 
4.6%
Other values (14) 1656
23.6%
Common
ValueCountFrequency (%)
1557
43.9%
0 1157
32.6%
9 455
 
12.8%
2 198
 
5.6%
- 107
 
3.0%
1 73
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10452
98.8%
None 124
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1557
14.9%
0 1157
 
11.1%
a 914
 
8.7%
e 729
 
7.0%
t 691
 
6.6%
n 517
 
4.9%
u 513
 
4.9%
r 506
 
4.8%
9 455
 
4.4%
s 420
 
4.0%
Other values (18) 2993
28.6%
None
ValueCountFrequency (%)
à 91
73.4%
é 33
 
26.6%

Interactions

2024-02-06T11:12:46.507629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.325454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.769576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.157918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.534058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.907801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.264956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.625596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.998078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.546222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.369830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.826443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.194532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.575706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.947672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.303231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.665947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.038214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.583925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.429254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.871257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.233523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.615586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.984725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.341497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.708817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.078812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.620996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.489109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.916239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.275555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.658982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.029143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.381419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.746317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.131875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.660937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.541163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.958254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.319666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.703499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.070771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.420102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.789898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.173691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.698588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.594176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.996410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.365158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.746688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.109812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.458409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.828877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.212954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.737430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.641331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.033686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.409275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.783794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.145400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.495248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.869276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.387487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.777912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.687831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.076864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.455115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.827330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.188033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.539111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.914297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.427950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.816535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:43.729991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.119505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.497238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:44.866585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.224574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.580280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:45.958199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-06T11:12:46.468904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-06T11:12:49.159495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
random_id4nbinstanbtwitnbsnapnbtikinstapsnappgoogpgoogmpsurveytps_rsinstagratwittersnapchattiktokCS05CS12resid2resid6
random_id41.0000.0010.0450.0870.001-0.016-0.006-0.008-0.0050.0000.0700.0000.0000.0000.0000.0000.0770.0000.000
nbinsta0.0011.0000.2790.3020.5340.1820.1420.076-0.0020.1540.0401.0000.1340.1700.2230.0000.0000.1730.080
nbtwit0.0450.2791.0000.1340.271-0.1670.034-0.141-0.1610.0000.0200.0001.0000.0000.0000.1480.0820.0000.114
nbsnap0.0870.3020.1341.0000.273-0.1060.034-0.043-0.0920.0830.0000.1600.0001.0000.0000.0000.0480.0000.000
nbtik0.0010.5340.2710.2731.0000.1290.127-0.021-0.1160.0620.0000.0000.0000.0351.0000.0000.0000.1540.000
instap-0.0160.182-0.167-0.1060.1291.0000.3130.2850.2860.0000.0750.0000.0000.0000.1480.0550.0660.1110.000
snapp-0.0060.1420.0340.0340.1270.3131.0000.1930.1670.0000.0000.0000.0000.1740.1680.0000.1240.0680.063
googp-0.0080.076-0.141-0.043-0.0210.2850.1931.0000.5820.0500.0000.0000.0640.0540.0460.0530.0000.0760.000
googmp-0.005-0.002-0.161-0.092-0.1160.2860.1670.5821.0000.0000.0000.0000.0150.0000.0370.0000.0000.1000.014
survey0.0000.1540.0000.0830.0620.0000.0000.0500.0001.0000.0000.0450.0620.0860.0590.0190.0770.3080.000
tps_rs0.0700.0400.0200.0000.0000.0750.0000.0000.0000.0001.0000.2800.1850.1470.3190.0770.0930.0750.020
instagra0.0001.0000.0000.1600.0000.0000.0000.0000.0000.0450.2801.0000.0740.1260.1010.0880.1820.0650.071
twitter0.0000.1341.0000.0000.0000.0000.0000.0640.0150.0620.1850.0741.0000.1440.1870.0530.0000.0000.084
snapchat0.0000.1700.0001.0000.0350.0000.1740.0540.0000.0860.1470.1260.1441.0000.2590.0730.0000.0000.215
tiktok0.0000.2230.0000.0001.0000.1480.1680.0460.0370.0590.3190.1010.1870.2591.0000.0000.0700.0000.000
CS050.0000.0000.1480.0000.0000.0550.0000.0530.0000.0190.0770.0880.0530.0730.0001.0000.2610.1120.095
CS120.0770.0000.0820.0480.0000.0660.1240.0000.0000.0770.0930.1820.0000.0000.0700.2611.0000.0000.038
resid20.0000.1730.0000.0000.1540.1110.0680.0760.1000.3080.0750.0650.0000.0000.0000.1120.0001.0000.176
resid60.0000.0800.1140.0000.0000.0000.0630.0000.0140.0000.0200.0710.0840.2150.0000.0950.0380.1761.000

Missing values

2024-02-06T11:12:46.876841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-06T11:12:46.973445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-06T11:12:47.061284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

random_id4surveytps_rsinstagranbinstatwitternbtwitsnapchatnbsnaptiktoknbtikinstapsnappgoogpgoogmpCS05CS12resid2resid6
037320ScPoBx_3A2.0J'ai188.0Je n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaN5.05.00.00.0Assez bienSouventBordeaux- de 20 000 habitants
175227ScPoBx_3A3.0J'ai400.0Je n'ai pasNaNJ'ai40.0J'ai100.00.00.00.00.0Assez bienSouventBordeauxAgglomération parisienne
278695ScPoBx_3A2.0J'ai353.0J'ai12.0Je n'ai pasNaNJ'ai271.03.00.010.03.0Très bienParfoisBordeauxCommune rurale
380831ScPoBx_1A2.0J'ai450.0Je n'ai pasNaNJ'ai4.0J'ai11.05.00.013.013.0Assez bienSouventPessac / Talence / Gradignan100 000 habitats et plus
4140145ScPoBx_1A4.0J'ai120.0Je n'ai pasNaNJ'ai10.0J'ai10.00.00.07.015.0Assez bienParfoisAutre commune- de 20 000 habitants
5142591ScPoBx_1A4.0J'ai1400.0Je n'ai pasNaNJe n'ai pasNaNJ'ai230.0NaNNaNNaNNaNAssez bienSouventPessac / Talence / GradignanAgglomération parisienne
6154015ScPoBx_3A3.0J'ai450.0Je n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaN3.00.010.01.0Assez bienParfoisAutre commune20 000 à 99 999 habitants
7157492ScPoBx_3A4.0J'ai170.0J'ai3.0Je n'ai pasNaNJe n'ai pasNaN0.00.010.010.0Assez bienParfoisPessac / Talence / GradignanCommune rurale
8180228ScPoBx_1A1.0J'ai200.0Je n'ai pasNaNJe n'ai pasNaNJ'ai15.05.00.015.020.0Assez bienParfoisBordeauxCommune rurale
9215268ScPoBx_1A2.0J'ai250.0Je n'ai pasNaNJ'ai20.0Je n'ai pasNaN2.01.05.02.0Assez bienParfoisPessac / Talence / GradignanAgglomération parisienne
random_id4surveytps_rsinstagranbinstatwitternbtwitsnapchatnbsnaptiktoknbtikinstapsnappgoogpgoogmpCS05CS12resid2resid6
5199741187ScPoBx_3A1.0J'ai130.0Je n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaN3.0NaNNaNNaNAssez bienSouventPessac / Talence / GradignanAutre pays que la France
5209745640ScPoBx_1A2.0J'ai500.0Je n'ai pasNaNJ'ai200.0Je n'ai pasNaN0.00.010.00.0Assez bienParfoisPessac / Talence / Gradignan- de 20 000 habitants
5219801565ScPoBx_1A3.0J'ai433.0Je n'ai pasNaNJ'aiNaNJ'ai22.05.05.05.00.0Assez bienParfoisPessac / Talence / Gradignan20 000 à 99 999 habitants
5229816676ScPoBx_3A1.0J'ai166.0Je n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaN0.00.05.02.0Très bienParfoisBordeauxAgglomération parisienne
5239897608ScPoBx_1A2.0J'ai180.0J'ai4.0Je n'ai pasNaNJe n'ai pasNaN5.00.00.02.0Assez bienParfoisBordeaux100 000 habitats et plus
5249928746ScPoBx_1A3.0J'ai100.0Je n'ai pasNaNJe n'ai pasNaNJ'ai4.00.00.00.00.0Très bienParfoisPessac / Talence / GradignanAgglomération parisienne
5259940978ScPoBx_1A1.0J'ai820.0J'ai7.0Je n'ai pasNaNJ'ai398.00.00.05.010.0Très bienParfoisPessac / Talence / Gradignan- de 20 000 habitants
5269947814ScPoBx_3A1.0J'ai717.0Je n'ai pasNaNJe n'ai pasNaNJ'ai1163.010.00.010.00.0Très bienParfoisBordeauxAutre pays que la France
5279956964ScPoBx_1A1.0Je n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaNJe n'ai pasNaN0.00.00.00.0Assez bienParfoisAutre commune- de 20 000 habitants
5289973576ScPoBx_3A2.0J'ai800.0J'ai150.0J'ai100.0Je n'ai pasNaN1.01.00.00.0Très bienParfoisBordeauxCommune rurale